数据分析是指用适当的统计方法对收集来的大量第一手资料和第二手资料进行分析,以求最大化地开发数据资料的功能,发挥数据的作用。

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R和Python语言是Power BI的强大补充。它们支持高级的数据转换技术,这些技术在Power BI的默认配置中很难执行,但通过利用R和Python的功能变得更容易。如果您是一位业务分析师、数据分析师或数据科学家,希望推动Power BI,并将其从一个商业智能工具转变为一个高级数据分析工具,那么这本书将帮助您实现这一目标。

你将学到什么

  • 使用ggplot2包通过R创建高级数据可视化
  • 使用R和Python摄取数据,以克服Power查询的一些限制
  • 使用R和Python对数据应用机器学习模型,而不需要Power BI premium compacity
  • 通过微软认知服务、IBM Watson自然语言理解和SQL Server机器学习服务中的预训练模型,在Power - - BI中加入高级人工智能,而不需要Power BI premium compacity
  • 使用R和Python执行Power BI中不可能执行的高级字符串操作

这本书是给谁的

  • 普通用户、数据分析师和数据科学家
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This paper presents a novel approach of representing dynamic visual scenes with static maps generated from video/image streams. Such representation allows easy visual assessment of motion in dynamic environments. These maps are 2D matrices calculated recursively, in a pixel-wise manner, that is based on the recently introduced concept of Eccentricity data analysis. Eccentricity works as a metric of a discrepancy between a particular pixel of an image and its normality model, calculated in terms of mean and variance of past readings of the same spatial region of the image. While Eccentricity maps carry temporal information about the scene, actual images do not need to be stored nor processed in batches. Rather, all the calculations are done recursively, based on a small amount of statistical information stored in memory, thus resulting in a very computationally efficient (processor- and memory-wise) method. The list of potential applications includes video-based activity recognition, intent recognition, object tracking, video description, and so on.

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